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modifcation to train and eval

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This commit is contained in:
s444415 2022-12-16 12:32:08 +00:00
parent 67cc4bdf7c
commit d98383197f
2 changed files with 23 additions and 19 deletions
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24
donut-eval.py
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@ -11,21 +11,21 @@ import json
import torch
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
from donut import JSONParseEvaluator
# In[2]:
processor = DonutProcessor.from_pretrained("Zombely/plwiki-proto-fine-tuned")
model = VisionEncoderDecoderModel.from_pretrained("Zombely/plwiki-proto-fine-tuned")
processor = DonutProcessor.from_pretrained("Zombely/plwiki-proto-fine-tuned-v2")
model = VisionEncoderDecoderModel.from_pretrained("Zombely/plwiki-proto-fine-tuned-v2")
# In[3]:
dataset = load_dataset("Zombely/pl-text-images-5000-whole", split="validation")
dataset = load_dataset("Zombely/diachronia-ocr", split='train')
# In[4]:
@ -38,11 +38,11 @@ model.to(device)
output_list = []
accs = []
has_metadata = bool(dataset[0].get('ground_truth'))
for idx, sample in tqdm(enumerate(dataset), total=len(dataset)):
# prepare encoder inputs
pixel_values = processor(sample["image"].convert("RGB"), return_tensors="pt").pixel_values
pixel_values = processor(sample['image'].convert("RGB"), return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
# prepare decoder inputs
task_prompt = "<s_cord-v2>"
@ -68,16 +68,20 @@ for idx, sample in tqdm(enumerate(dataset), total=len(dataset)):
seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
seq = re.sub(r"<.*?>", "", seq, count=1).strip() # remove first task start token
seq = processor.token2json(seq)
if has_metadata:
ground_truth = json.loads(sample["ground_truth"])
ground_truth = ground_truth["gt_parse"]
evaluator = JSONParseEvaluator()
score = evaluator.cal_acc(seq, ground_truth)
accs.append(score)
print(seq)
output_list.append(seq)
df = pd.DataFrame(map(lambda x: x.get('text_sequence', ''), output_list))
df.to_csv('out.tsv', sep='\t', header=False, index=False)
scores = {"accuracies": accs, "mean_accuracy": np.mean(accs)}
print(scores, f"length : {len(accs)}")
print("Mean accuracy:", np.mean(accs))
if has_metadata:
scores = {"accuracies": accs, "mean_accuracy": np.mean(accs)}
print(scores, f"length : {len(accs)}")
print("Mean accuracy:", np.mean(accs))

8
donut-train.py
View File

@ -22,7 +22,7 @@ from pytorch_lightning.plugins import CheckpointIO
DATASET_PATH = "Zombely/pl-text-images-5000-whole"
PRETRAINED_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned"
PRETRAINED_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
START_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned"
OUTPUT_MODEL_PATH = "Zombely/plwiki-proto-fine-tuned-v2"
LOGGING_PATH = "plwiki-proto-ft-second-iter"
@ -30,8 +30,8 @@ CHECKPOINT_PATH = "./checkpoint"
train_config = {
"max_epochs":30,
"val_check_interval":0.5, # how many times we want to validate during an epoch
"max_epochs":1,
"val_check_interval":1.0, # how many times we want to validate during an epoch
"check_val_every_n_epoch":1,
"gradient_clip_val":1.0,
"num_training_samples_per_epoch": 800,
@ -339,7 +339,7 @@ class PushToHubCallback(Callback):
login(os.environ.get("HUG_TOKKEN", ""))
login(os.environ.get("HUG_TOKKEN", None), True)
# ### Wandb.ai link: https://wandb.ai/michalkozlowski936/Donut?workspace=user-michalkozlowski936